Evolution and co-evolution of computer programs to control independently-acting agents
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Building Large Knowledge-Based Systems; Representation and Inference in the Cyc Project
Hierarchical genetic algorithms operating on populations of computer programs
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Meta-Lamarckian learning in memetic algorithms
IEEE Transactions on Evolutionary Computation
Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming
Expert Systems with Applications: An International Journal
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In computational intelligence, the term 'memetic algorithm' has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a 'meme' has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as 'memetic algorithm' is too specific, and ultimately a misnomer, as much as a 'meme' is defined too generally to be of scientific use. In this paper, we extend the notion of memes from a computational viewpoint and explore the purpose, definitions, design guidelines and architecture for effective memetic computing. Utilizing two genetic programming test-beds (the even-parity problem and the Pac-Man video game), we demonstrate the power of high-order meme-based learning, known as meta-learning. With applications ranging from cognitive science to machine learning, meta-learning has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning.